annoy.

The nearest neighbor problem is very common in data science. It's useful in recommender situations but also with neural embeddings in general. It's an expensive thing to calculate so it is common to calculate approximate distances as a proxy. In python a very likeable tool for this is annoy.

Intro Code Plotting How it Works 1 How it Works 2 Accuracy Benchmark Final Features

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Notes

All the code below will generate the same plot;

import numpy as np
import matplotlib.pylab as plt
from annoy import AnnoyIndex

columns = 2

vecs = np.concatenate([
    np.random.normal(-1, 1, (5000, columns)), 
    np.random.normal(0, 0.5, (5000, columns)),
])

annoy = AnnoyIndex(columns, 'euclidean')
for i in range(vecs.shape[0]):
    annoy.add_item(i, vecs[i, :])
annoy.build(n_trees=1)

plt.figure(figsize=(5, 5))
plt.scatter(vecs[:, 0], vecs[:, 1], s=1);

indices = annoy.get_nns_by_vector(np.array([-1., -1.]), 2000)

subset = vecs[indices, :]
plt.scatter(subset[:, 0], subset[:, 1], s=1);

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